import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
from joblib import load

class NN(object):
    def __init__(self):
        self.loaded_model = tf.keras.models.load_model('timing/my_model.keras')
        self.scaler = load('timing/scaler.joblib')

    def get_hourly_trend(self):
        n_steps = 7
        df_pivot = pd.read_csv('timing/scenic_data.csv')
        x_values = df_pivot.iloc[-n_steps:]
        x_values.iloc[-1, x_values.columns.get_loc('count')] = 0
        latest_data = x_values.values  # 取最后七天数据
        latest_data = latest_data.reshape(4, n_steps, latest_data.shape[1])

        # 预测与反归一化
        predicted = self.loaded_model.predict(latest_data)
        predicted_counts = self.scaler.inverse_transform(predicted)  # 反归一化
        predicted_counts[predicted_counts < 0] = 0
        hourly_trend = predicted_counts[0].astype(int).tolist()

        print(hourly_trend)

if __name__ == '__main__':
    nn = NN()
    nn.get_hourly_trend()